continuous_table {KODAMA} | R Documentation |
Summarization of the continuous information.
continuous_table (name,num, label, digits=0,scientific=FALSE)
name |
the name of the feature. |
num |
the information to summarize. |
label |
the classification of the cohort. |
digits |
how many significant digits are to be used. |
scientific |
either a logical specifying whether result should be encoded in scientific format. |
The function returns a table with the summarized information. If the number of group is equal to two, the p-value is computed using the Wilcoxon rank-sum test, Kruskal-Wallis test otherwise.
Stefano Cacciatore
Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link
Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA
KODAMA: an updated R package for knowledge discovery and data mining.
Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. Link
data(clinical) A=categorical_table("Gender",clinical[,"Gender"],clinical[,"Hospital"]) B=categorical_table("Gleason score",clinical[,"Gleason score"],clinical[,"Hospital"]) C=categorical_table("Ethnicity",clinical[,"Ethnicity"],clinical[,"Hospital"]) D=continuous_table("BMI",clinical[,"BMI"],clinical[,"Hospital"],digits=2) E=continuous_table("Age",clinical[,"Age"],clinical[,"Hospital"],digits=1) rbind(A,B,C,D,E)